1 files changed, 4 insertions, 4 deletions

diff --git a/training/optimization.py b/training/optimization.py
index 012beed..0fd7ec8 100644
--- a/training/optimization.py
+++ b/training/optimization.py
@@ -6,7 +6,7 @@ from diffusers.utils import logging
 logger = logging.get_logger(__name__)
 
 
-def get_one_cycle_schedule(optimizer, num_training_steps, annealing="cos", min_lr=0.05, mid_point=0.42, last_epoch=-1):
+def get_one_cycle_schedule(optimizer, num_training_steps, annealing="cos", min_lr=0.05, mid_point=0.43, last_epoch=-1):
     """
     Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
     a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
@@ -33,10 +33,10 @@ def get_one_cycle_schedule(optimizer, num_training_steps, annealing="cos", min_l
             if current_step < thresh_down:
                 return min_lr + float(thresh_down - current_step) / float(max(1, thresh_down - thresh_up)) * (1 - min_lr)
 
-            return max(0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - thresh_down))) * min_lr
+            progress = float(num_training_steps - current_step) / float(max(1, num_training_steps - thresh_down))
+            return max(0.0, progress) * min_lr
         else:
             progress = float(current_step - thresh_up) / float(max(1, num_training_steps - thresh_up))
-
-            return max(0.0, 0.5 * (1.0 + math.cos(math.pi * progress)))
+            return max(0.0, 1.0 + math.cos(math.pi * (0.5 + 0.5 * progress)))
 
     return LambdaLR(optimizer, lr_lambda, last_epoch)